By: Shivaramakrishnan Iyer, Technology Partner, Canvas by LTIMindtree. GTO

AI and Machine Learning aided software development, is it a norm or an extravaganza? Is this the future of software engineering? This blog helps you to dig deep into the topic.

An employee at Google recently had been in the news for indicating that Artificial Intelligence (AI) initiatives within Google were becoming ‘sentient’ – meaning life-like. This incident once again drew me back to a Self-Aware Software System blog I had written some time back.

This article explicitly highlights aspects of the software engineering discipline and the engineer/developers who should leverage both Machine Learning (ML) and AI areas to improve.

Over the next decade, the wave of Machine Learning and data science in software engineering will disrupt and transform the information technology industry in a significant manner, including the way we develop our software.

Organizations that build better software engineering tools are investing heavily in these areas and will need a growing number of machine learning and data science experts shortly.

Machine learning within software engineering development seems like a new fad. However, it will become more conspicuous due to the availability of vast amounts of software engineering data and more affordable computing power.

Much of the malaise in building a good tool for usage within the software delivery lifecycle is that multiple tools across the process must be integrated. Each has its own quirks and experiences across the personas, including clients, non-technical members, software engineers, product designers, quality assurance engineers, security engineers, configuration and deployment engineers, infrastructure providers, production support engineers, and project managers.

The success in using machine learning for tools needed for the software engineering discipline depends on the following:
1. Building both effective and efficient infrastructure
2. Using the correct toolkit
3. Applying the right algorithms to support across the entire lifecycle, better known as DevSecOps

Below are some of the promising machine learning aspects that will be leveraged in building future software development and delivery lifecycle tools.

Algorithms

Algorithms have their origins back in the 1970s. Primarily these are used to accelerate the build or development of code through autonomous insights enabled by automated pre-programmed instructions. This helps a software engineer make rapid and relevant engineering decisions.

Machine learning pushes algorithms to new levels, can be employed and adapted in real-time, and can offer unique insights to software developers for better productivity and improved quality.

Robo-advisors

While not fully disclosing their machine learning approaches to software engineering, many technology-centric organizations like Google promote good practices by making key components such as co-buddy or companion bots available.

Software engineering firms are exploring potential AI solutions for improving engineering decisions and using troves of historical data across software development in multiple languages, including legacy codebases.

One example of this is the use of Robo-advisors, which essentially are algorithms to ensure that software engineering outcomes can be done quickly. These co-pilots or buddies can provide automated guidance and service to the developers using an IDE to develop an application.

A developer starts building some lines of code, and the advisor immediately prompts up and tells all the classes or functions available to ensure that the developer’s approach to the functionality or even a non-functional area needs to be addressed. The system then ensures that the entire code for that functionality is available in real-time, always aiming to find the best fit.

Robo-advisors have gained significant traction among developers who do not need a human peer review to feel comfortable developing as a pair of programmers. An example is the GitHub Co-pilot, an AI tool that automatically assists engineer developers in providing suggestions.

Early defect identification and prevention

Defects are a massive problem for any software engineering development team and are one of the foremost reasons to leverage machine learning in the area of defect identification and prevention.

Previous defect detection systems depended heavily on a complex and robust set of rules that also needed significant manual effort. Modern defect detection and prevention go beyond following a checklist of factors contributing to defects. It actively learns and calibrates new potential defects and can be automated to self-remediate and self-heal in some cases.

Machine learning is most suited to combat defects within software engineering functionality. This is because machine-learning systems can scan through vast datasets of codes and related best coding practices, detect unusual activities, and flag them instantly. Given the countless ways that detection occurs at any point in time, machine learning systems should be an absolute necessity in the days to come for augmenting software engineering discipline and test strategy. In the not-so-distant future, such ML-assisted functionality can self-correct code to respond to defects.

Automation

Automation is aptly suited for software engineering. Many repetitive low-value tasks for software engineers can be addressed easily by optimizing routine, everyday processes enabling engineering teams to focus on high-value work. Additional other benefits are savings in terms of cost and production time.

Adding machine learning and AI to the automation list can provide engineers with a different additional support layer. With access to relevant data, machine learning and AI can provide accelerated insights with excellent data analysis for entire development teams to deliberate on critical decisions. In the not-so-distant future, the recommendation of the best course of action through collaboration can even reduce the need for pair programming.

AI and automation in software engineering can also learn to recognize errors reducing the time wasted between discovery and resolution. This means that team members are less likely to be delayed in completing their work with better quality. A natural evolution would be companion chatbots that could support engineering across the entire software delivery lifecycle of actors and collaborators.

Developer performance prediction

Developer performance prediction will emerge as a sophisticated area within software engineering and software delivery lifecycle in predicting developer performance to allow someone to understand the factors that drive developers and speculate on the performance of their code.

Traditionally developer performance prediction is performed by analyzing coding techniques against programming practices. One such example is the approach for a specific function leveraging various classes and interfaces that rely on static analysis tools.

With the tremendous increase in software engineering data available, approaches for analysis leveraging ML and AI will be emergent.

This is not to state that AI will start writing code, but it paves the way for software engineers and developers to leverage AI to write better code. For example, the software engineer might leverage AI for huge complex areas while concentrating on other challenging high-value ones.

Real-time sentiment analysis

With the availability of past and real-time data of the codes, coding styles, and volumes of unstructured data such as videos, transcriptions, images, audio files, many articles, and codes, including business documents, NFRS, functional requirements, tests, observability data, etc., AI can understand and make better recommendation across the software delivery lifecycle.

Many of the existing video conferencing or collaboration software have embedded real-time feedback to enhance the experience. AI-accelerated insights as part of the available real-time collaboration software can change how our software engineers interact and use key functionality. ML algorithms can learn and serve relevant and variable content, creating a diverse and enriched experience for the engineer by providing recommendations on focus areas.

Another use of such sentiment analysis within software engineering is the ability to tie in budget, schedule, or effort estimates through extensive analysis of past data specifically related to functional and non-functional requirements. It helps in predicting the behavior and possible trade-offs required for customer proposals.

The developer community moves in response to a myriad of human-related factors and hopes that machine learning will be able to replicate and enhance human intuition about software engineering itself by discovering new trends and telling signals.

However, much of the future applications of machine learning will be in understanding and predicting how these requirements, specific trends, and real-time details like collaboration and team events enable software developers’ sentiments towards making better code.

It will not be limited to predicting the use or usage of better code and building the right software engineering practices. Error management is an important area to understand and reduce downtime for SaaS-based applications. It can predict and automate to detect and self-remediate without human intervention and will become the future.

The future is software-eating software!

Marc Andreesen had famously espoused ‘software is eating the world.’ AI can be leveraged to provide insights into building better software. It will become a necessity for software engineers to use ML and AI for their work. As the entire software community starts to use AI-accelerated insights, customers will be the ultimate beneficiaries as they get improved software quality at a better and more optimized cost. The value chain becomes validated and emerges stronger thanks to ML and AI.